As indicated in previous works (e.g. [1]}) detecting faults in machine learning models can be difficult. This is especially true when models are used in sensitive decision-making processes, where mistakes can have serious consequences.
Despite the sensitivity of the matter, many ML models go into production unmonitored and without proper testing, and thus they are prone to significant risks.
